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can an AI be programmed with language other than lisp?? RRS feed

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  • In prolog, you can create AI programs. Smile
    Wednesday, February 28, 2007 12:35 PM
  • Sanket is right.... I have seen a prolog program to solve the famous "monkey & banana" problem.
    Wednesday, March 14, 2007 2:41 PM
  • whats list?

    Saturday, March 17, 2007 9:37 AM
  • in prolog u can write cutting edge programs of AI.
    Monday, March 19, 2007 12:53 PM
  • hey! this greek and latin is getting too much for me, can anyone help me out?
    what is lisp and prolog?
    Monday, March 19, 2007 6:22 PM
  • Using Prolog we can even veriy the string arthimtics
    Friday, March 23, 2007 3:04 PM
  • but these r old languages..

    in this tech. era

    dont v hav any latest lang. for our future revolution creating suvbject AI????????

    Saturday, March 31, 2007 6:43 PM
  • yes u can do in other languages other tahn LISP too
    Monday, April 2, 2007 12:56 PM
  • dear friend i think snowball is another lang. to prog in AI
    Sunday, April 15, 2007 6:15 PM
  • THANKS
    Saturday, April 28, 2007 12:46 PM
  • Hi,

     

    Yes, AI Programming can be done with any programming language that offers the implementation of Data Structures, for AI needs one to succesfully implement algorithms which have a major usage of data structures like HashTables, Binary Trees etc. Only recently, I have completed developing a NLP (Natural Language Processing) project using Java.

     However, I would recommend using python as the base language, as it has all the features that Lisp or Prolog provides as well as platform independence and reusable codes and APIs like java. Further, it is also possible to perform GUI development in python and the coding is easy to learn and implement.

     

    Regards,

    Arijit Chatterjee

    Wednesday, May 9, 2007 3:46 PM
  • thank dear for ur sug
    Thursday, May 10, 2007 7:54 AM
  • or those interested in the enterprise of understanding intelligence AI programming involves working in domains for which the problem is often poorly understood - hence the design-code cycle is often very tightly coupled. This gives rise to a need for flexible environments and languages capable of supporting rapid changes in knowledge representation schemes and alterations to the inference processes at work.

    Those interested in the engineering aspect of AI are in a similar position though the problems faced are somewhat different as the engineering approach is primarily concerned with reasoning about formal systems (and, less directly, about the world as well). In this case, the same concerns surface: a need for flexible environments to support a tight design-code cycle; a need to accommodate changes in knowledge representation; and a need to be able to change inference processes.

    The difference between these two views of AI programming is significant. In the engineering context the problem is often very well understood but the solution is not. In the `scientific' approach, a major issue is the representation of the problem itself. Often, one of the main results of such work is to clarify the precise nature of the problem.

    Turning to the support needed for these two styles of AI work, AI languages have traditionally been ones which stress: a) knowledge representation schemes; b) pattern matching; c) flexible search; and d) programs as data.

    The standard examples are LISP, Pop-2 (which developed into Pop11) and Prolog. Both LISP and Prolog are based on foundational work on logic. LISP is a functional language based on the lambda calculus. Prolog is a relational language based on standard first order predicate logic. Both are extended to account for `awkward' issues in programming such as input/output and programs that learn etc (awkward from a logical point of view). Pop11 is a stack-based language of great flexibility which has some similarity to LISP.

    Pop11 is embedded in an AI Programming Environment called Poplog which permits the mixed use of a variety of programming languages - including Prolog, Common LISP and ML. This approach supports a flexible use of ``implementation'' languages. For example, a programmer can exploit Prolog to develop an automated theorem prover and Pop11 to perform a complex image analysis task.

    Environments such as Poplog provide flexible implementation support. Other sophisticated hybrid programming environments exist which stress the domain level (as opposed to the implementation level). The KLONE family of languages support modelling at the ``knowledge level''. Environments such as KEE, ART and CLIPS all provide a variety of knowledge representation schemes - allowing the AI programmer to integrate object oriented representations with ones based on logic.

    There are numerous descendents of such environments and many sophisticated programming languages. However, AI programming can exploit any language from BASIC through C to Smalltalk - though the ease with which languages can be exploited depends on what stage of development an AI system is at.

    Thursday, May 31, 2007 9:13 AM
  • This topic can be somewhat sensitive, so I'll probably tread on a few
    toes, please forgive me. There is no authoritative answer for this
    question, as it really depends on what languages you like programming
    in. AI programs have been written in just about every language ever
    created. The most common seem to be Lisp, Prolog, C/C++, recently
    Java, and even more recently, Python.

    LISP- For many years, AI was done as research in universities and
    laboratories, thus fast prototyping was favored over fast execution.
    This is one reason why AI has favored high-level langauges such as
    Lisp. This tradition means that current AI Lisp programmers can draw
    on many resources from the community. Features of the language that
    are good for AI programming include: garbage collection, dynamic
    typing, functions as data, uniform syntax, interactive environment,
    and extensibility. Read Paul Graham's essay, "Beating the Averages"
    for a discussion of some serious advantages:
    http://www.paulgraham.com/avg.html

    PROLOG- This language wins 'cool idea' competition. It wasn't until
    the 70s that people began to realize that a set of logical statements
    plus a general theorem prover could make up a program. Prolog
    combines the high-level and traditional advantages of Lisp with a
    built-in unifier, which is particularly useful in AI. Prolog seems to
    be good for problems in which logic is intimately involved, or whose
    solutions have a succinct logical characterization. Its major
    drawback (IMHO) is that it's hard to learn.

    C/C++- The speed demon of the bunch, C/C++ is mostly used when the
    program is simple, and excecution speed is the most important.
    Statistical AI techniques such as neural networks are common examples
    of this. Backpropagation is only a couple of pages of C/C++ code, and
    needs every ounce of speed that the programmer can muster.

    Java- The newcomer, Java uses several ideas from Lisp, most notably
    garbage collection. Its portability makes it desirable for just about
    any application, and it has a decent set of built in types. Java is
    still not as high-level as Lisp or Prolog, and not as fast as C,
    making it best when portability is paramount.

    Python- This language does not have widespread acceptance yet, but
    several people have suggested to me that it might end up passing Java
    soon. Apparently the new edition of the Russell-Norvig textbook will
    include Python source as well as Lisp. According to Peter Norvig,
    "Python can be seen as either a practical (better libraries) version
    of Scheme, or as a cleaned-up (no $@&%) version of Perl." For more
    information, especially on how Python compares to Lisp, go to
    http://norvig.com/python-lisp.html

    AI programs have been written in just about every language ever
    created.  The most common seem to be Lisp, Prolog, C/C++,  recently
    Java, and even more recently, Python.
    
    LISP- For many years, AI was done as research in universities and
    laboratories, thus fast prototyping was favored over fast execution.
    This is one reason why AI has favored high-level langauges such as
    Lisp.  This tradition means that current AI Lisp programmers can draw
    on many resources from the community.  Features of the language that
    are good for AI programming include: garbage collection, dynamic
    typing, functions as data, uniform syntax, interactive environment,
    and extensibility. Read Paul Graham's essay, "Beating the Averages"
    for a discussion of some serious advantages:
    http://www.paulgraham.com/avg.html
    
    PROLOG- This language wins 'cool idea' competition.  It wasn't until
    the 70s that people began to realize that a set of logical statements
    plus a general theorem prover could make up a program.  Prolog
    combines the high-level and traditional advantages of Lisp with a
    built-in unifier, which is particularly useful in AI.  Prolog seems to
    be good for problems in which logic is intimately involved, or whose
    solutions have a succinct logical characterization.  Its major
    drawback (IMHO) is that it's hard to learn.
    
    C/C++- The speed demon of the bunch, C/C++ is mostly used when the
    program is simple, and excecution speed is the most important.
    Statistical AI techniques such as neural networks are common examples
    of this.  Backpropagation is only a couple of pages of C/C++ code, and
    needs every ounce of speed that the programmer can muster.
    
    Java- The newcomer, Java uses several ideas from Lisp, most notably
    garbage collection.  Its portability makes it desirable for just about
    any application, and it has a decent set of built in types.  Java is
    still not as high-level as Lisp or Prolog, and not as fast as C,
    making it best when portability is paramount.
    
    Python- This language does not have widespread acceptance yet, but
    several people have suggested to me that it might end up passing Java
    soon.  Apparently the new edition of the Russell-Norvig textbook will
    include Python source as well as Lisp.  According to Peter Norvig,
    "Python can be seen as either a practical (better libraries) version
    of Scheme, or as a cleaned-up (no $@&%) version of Perl."  For more
    information, especially on how Python compares to Lisp, go to
    http://norvig.com/python-lisp.html
    Thursday, May 31, 2007 9:16 AM
  • oh yes.
    you can use any language.
    but prolog is much popular because it is simple to use
    Thursday, May 31, 2007 2:09 PM
  • yes.
    Wednesday, June 13, 2007 4:02 AM
  • What about Clips haven't any of  heard of it ?

    Sunday, September 27, 2009 10:09 AM